| Autor: |
Di Chen, Lixia Xu, Huiwu Xing, Weitao Shen, Ziguang Song, Hongjiang Li, Xuqiang Zhu, Xueyuan Li, Lixin Wu, Henan Jiao, Shuang Li, Jing Yan, Yuting He, Dongming Yan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
iMeta, Vol 3, Iss 5, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2770-596X |
| DOI: |
10.1002/imt2.238 |
| Popis: |
Abstract In recent years, development in high‐throughput sequencing technologies has experienced an increasing application of statistics, pattern recognition, and machine learning in bioinformatics analyses. SangeBox platform to meet different scientific demands. The new version of Sangs is a widely used tool among many researchers, which encourages us to continuously improve the plerBox 2 (http://vip.sangerbox.com) and extends and optimizes the functions of interactive graphics and analysis of clinical bioinformatics data. We introduced novel analytical tools such as random forests and support vector machines, as well as corresponding plotting functions. At the same time, we also optimized the performance of the platform and fixed known problems to allow users to perform data analyses more quickly and efficiently. SangerBox 2 improved the speed of analysis, reduced resource required for computer performance, and provided more analysis methods, greatly promoting the research efficiency. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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